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PySpark Cookbook

You're reading from   PySpark Cookbook Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788835367
Length 330 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Denny Lee Denny Lee
Author Profile Icon Denny Lee
Denny Lee
Tomasz Drabas Tomasz Drabas
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Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Title Page
Packt Upsell
Contributors
Preface
1. Installing and Configuring Spark 2. Abstracting Data with RDDs FREE CHAPTER 3. Abstracting Data with DataFrames 4. Preparing Data for Modeling 5. Machine Learning with MLlib 6. Machine Learning with the ML Module 7. Structured Streaming with PySpark 8. GraphFrames – Graph Theory with PySpark Index

Overview of DataFrame actions


Transformations listed in the previous recipe transform one DataFrame into another. However, they only get executed once an action is called on a DataFrame.

In this recipe, we will provide an overview of the most popular actions.

Getting ready

To execute this recipe, you need to have a working Spark 2.3 environment. You should have gone through the previous recipe, Specifying schema programmatically, as we will be using the sample_data_schema DataFrame we created there.

There are no other requirements.

How to do it...

In this section, we will list some of the most common actions available for DataFrames. The purpose of this list is not to provide a comprehensive enumeration of all available transformations, but to give you some intuition behind the most common ones.

The .show(...) action

The .show(...) action, by default, shows the top five rows in tabular form. You can specify how many records to retrieve by passing an integer as a parameter.

Look at the following code...

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